lab6
Abigail Booth
September 28, 2020
Building Maps
library(tidyverse)
library(maps)
library(mapdata)
library(lubridate)
library(viridis)
library(wesanderson)daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>%
rename(Long = "Long_") ## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_character(),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character()
## )ggplot(daily_report, aes(x = Long, y = Lat, size = Confirmed/1000)) +
borders("world", colour = NA, fill = "grey90") +
theme_bw() +
geom_point(shape = 21, color='purple', fill='purple', alpha = 0.5) +
labs(title = 'World COVID-19 Confirmed cases',x = '', y = '',
size="Cases (x1000))") +
theme(legend.position = "right") +
coord_fixed(ratio=1.5)## Warning: Removed 54 rows containing missing values (geom_point).
daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-05-2020.csv")) %>%
rename(Long = "Long_") %>%
filter(Country_Region == "US") %>%
filter (!Province_State %in% c("Alaska","Hawaii", "American Samoa",
"Puerto Rico","Northern Mariana Islands",
"Virgin Islands", "Recovered", "Guam", "Grand Princess",
"District of Columbia", "Diamond Princess")) %>%
filter(Lat > 0)## Parsed with column specification:
## cols(
## FIPS = col_character(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character()
## )ggplot(daily_report, aes(x = Long, y = Lat, size = Confirmed/1000)) +
borders("state", colour = "black", fill = "grey90") +
theme_bw() +
geom_point(shape = 21, color='purple', fill='purple', alpha = 0.5) +
labs(title = 'COVID-19 Confirmed Cases in the US', x = '', y = '',
size="Cases (x1000))") +
theme(legend.position = "right") +
coord_fixed(ratio=1.5)
mybreaks <- c(1, 100, 1000, 10000, 10000)
ggplot(daily_report, aes(x = Long, y = Lat, size = Confirmed)) +
borders("state", colour = "white", fill = "grey90") +
geom_point(aes(x=Long, y=Lat, size=Confirmed, color=Confirmed),stroke=F, alpha=0.7) +
scale_size_continuous(name="Cases", trans="log", range=c(1,7),
breaks=mybreaks, labels = c("1-99",
"100-999", "1,000-9,999", "10,000-99,999", "50,000+")) +
scale_color_viridis_c(option="viridis",name="Cases",
trans="log", breaks=mybreaks, labels = c("1-99",
"100-999", "1,000-9,999", "10,000-99,999", "50,000+")) +
# Cleaning up the graph
theme_void() +
guides( colour = guide_legend()) +
labs(title = "Anisa Dhana's lagout for COVID-19 Confirmed Cases in the US'") +
theme(
legend.position = "bottom",
text = element_text(color = "#22211d"),
plot.background = element_rect(fill = "#ffffff", color = NA),
panel.background = element_rect(fill = "#ffffff", color = NA),
legend.background = element_rect(fill = "#ffffff", color = NA)
) +
coord_fixed(ratio=1.5)## Warning: Transformation introduced infinite values in discrete y-axis
## Warning: Transformation introduced infinite values in discrete y-axis## Warning in sqrt(x): NaNs produced## Warning: Removed 40 rows containing missing values (geom_point).
Mapping data to shapes
daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>%
rename(Long = "Long_") %>%
filter(Country_Region == "US") %>%
group_by(Province_State) %>%
summarize(Confirmed = sum(Confirmed)) %>%
mutate(Province_State = tolower(Province_State))## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_character(),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character()
## )## `summarise()` ungrouping output (override with `.groups` argument)# load the US map data
us <- map_data("state")
# We need to join the us map data with our daily report to make one data frame/tibble
state_join <- left_join(us, daily_report, by = c("region" = "Province_State"))# plot state map
ggplot(data = us, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
# Add data layer
geom_polygon(data = state_join, aes(fill = Confirmed), color = "black") +
scale_fill_gradientn(colours =
wes_palette("Zissou1", 100, type = "continuous"),
trans = "log10") +
labs(title = "COVID-19 Confirmed Cases in the US'")
library(RColorBrewer)
# To display only colorblind-friendly brewer palettes, specify the option colorblindFriendly = TRUE as follow:
# display.brewer.all(colorblindFriendly = TRUE)
# Get and format the covid report data
report_03_27_2020 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>%
rename(Long = "Long_") %>%
unite(Key, Admin2, Province_State, sep = ".") %>%
group_by(Key) %>%
summarize(Confirmed = sum(Confirmed)) %>%
mutate(Key = tolower(Key))## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_character(),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character()
## )## `summarise()` ungrouping output (override with `.groups` argument)# dim(report_03_27_2020)
# get and format the map data
us <- map_data("state")
counties <- map_data("county") %>%
unite(Key, subregion, region, sep = ".", remove = FALSE)
# Join the 2 tibbles
state_join <- left_join(counties, report_03_27_2020, by = c("Key"))
# sum(is.na(state_join$Confirmed))
ggplot(data = us, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
# Add data layer
borders("state", colour = "black") +
geom_polygon(data = state_join, aes(fill = Confirmed)) +
scale_fill_gradientn(colors = brewer.pal(n = 5, name = "PuRd"),
breaks = c(1, 10, 100, 1000, 10000, 100000),
trans = "log10", na.value = "White") +
ggtitle("Number of Confirmed Cases by US County") +
theme_bw() ## Warning: Transformation introduced infinite values in discrete y-axis
daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/04-02-2020.csv")) %>%
rename(Long = "Long_") %>%
filter(Province_State == "Massachusetts") %>%
group_by(Admin2) %>%
summarize(Confirmed = sum(Confirmed)) %>%
mutate(Admin2 = tolower(Admin2))## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_character(),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character()
## )## `summarise()` ungrouping output (override with `.groups` argument)us <- map_data("state")
ma_us <- subset(us, region == "massachusetts")
counties <- map_data("county")
ma_county <- subset(counties, region == "massachusetts")
state_join <- left_join(ma_county, daily_report, by = c("subregion" = "Admin2"))
# plot state map
ggplot(data = ma_county, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
# Add data layer
geom_polygon(data = state_join, aes(fill = Confirmed), color = "white") +
scale_fill_gradientn(colors = brewer.pal(n = 5, name = "BuGn"),
trans = "log10") +
labs(title = "COVID-19 Confirmed Cases in Massachusetts'")
daily_report## # A tibble: 14 x 2
## Admin2 Confirmed
## <chr> <dbl>
## 1 barnstable 283
## 2 berkshire 213
## 3 bristol 424
## 4 dukes and nantucket 12
## 5 essex 1039
## 6 franklin 85
## 7 hampden 546
## 8 hampshire 102
## 9 middlesex 1870
## 10 norfolk 938
## 11 plymouth 621
## 12 suffolk 1896
## 13 unassigned 270
## 14 worcester 667Interactive graphs
library(plotly)## Warning: package 'plotly' was built under R version 3.6.3##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutggplotly(
ggplot(data = ma_county, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
# Add data layer
geom_polygon(data = state_join, aes(fill = Confirmed), color = "black") +
scale_fill_gradientn(colours =
wes_palette("Zissou1", 100, type = "continuous")) +
ggtitle("COVID-19 Cases in MA") +
# Cleaning up the graph
labs(x=NULL, y=NULL) +
theme(panel.border = element_blank()) +
theme(panel.background = element_blank()) +
theme(axis.ticks = element_blank()) +
theme(axis.text = element_blank())
)## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.# Read in the daily report
daily_report <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>%
rename(Long = "Long_") %>%
group_by(Country_Region) %>%
summarize(Confirmed = sum(Confirmed), Deaths = sum(Deaths))## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )## `summarise()` ungrouping output (override with `.groups` argument)# Read in the world map data
world <- as_tibble(map_data("world"))
# Check to see if there are differences in the naming of countries
setdiff(world$region, daily_report$Country_Region) ## [1] "Aruba" "Anguilla"
## [3] "American Samoa" "Antarctica"
## [5] "French Southern and Antarctic Lands" "Antigua"
## [7] "Barbuda" "Saint Barthelemy"
## [9] "Bermuda" "Ivory Coast"
## [11] "Democratic Republic of the Congo" "Republic of Congo"
## [13] "Cook Islands" "Cape Verde"
## [15] "Curacao" "Cayman Islands"
## [17] "Czech Republic" "Canary Islands"
## [19] "Falkland Islands" "Reunion"
## [21] "Mayotte" "French Guiana"
## [23] "Martinique" "Guadeloupe"
## [25] "Faroe Islands" "Micronesia"
## [27] "UK" "Guernsey"
## [29] "Greenland" "Guam"
## [31] "Heard Island" "Isle of Man"
## [33] "Cocos Islands" "Christmas Island"
## [35] "Chagos Archipelago" "Jersey"
## [37] "Siachen Glacier" "Kiribati"
## [39] "Nevis" "Saint Kitts"
## [41] "South Korea" "Saint Martin"
## [43] "Marshall Islands" "Macedonia"
## [45] "Myanmar" "Northern Mariana Islands"
## [47] "Montserrat" "New Caledonia"
## [49] "Norfolk Island" "Niue"
## [51] "Bonaire" "Sint Eustatius"
## [53] "Saba" "Nauru"
## [55] "Pitcairn Islands" "Palau"
## [57] "Puerto Rico" "North Korea"
## [59] "Madeira Islands" "Azores"
## [61] "Palestine" "French Polynesia"
## [63] "South Sandwich Islands" "South Georgia"
## [65] "Saint Helena" "Ascension Island"
## [67] "Solomon Islands" "Saint Pierre and Miquelon"
## [69] "Swaziland" "Sint Maarten"
## [71] "Turks and Caicos Islands" "Turkmenistan"
## [73] "Tonga" "Trinidad"
## [75] "Tobago" "Taiwan"
## [77] "USA" "Vatican"
## [79] "Grenadines" "Saint Vincent"
## [81] "Virgin Islands" "Vanuatu"
## [83] "Wallis and Futuna" "Samoa"# Many of these countries are considered states or territories in the JHU covid reports,
# but let's fix a few of them
world <- as_tibble(map_data("world")) %>%
mutate(region = str_replace_all(region, c("USA" = "US", "Czech Republic" = "Czechia",
"Ivory Coast" = "Cote d'Ivoire", "Democratic Republic of the Congo" = "Congo (Kinshasa)",
"Republic of Congo" = "Congo (Brazzaville)")))
# Join the covid report with the map data
country_join <- left_join(world, daily_report, by = c("region" = "Country_Region"))
# Create the graph
ggplotly(
ggplot(data = world, mapping = aes(x = long, y = lat, text = region, group = group)) +
coord_fixed(1.3) +
# Add data layer
geom_polygon(data = country_join, aes(fill = Deaths), color = "black") +
scale_fill_gradientn(colours =
wes_palette("Zissou1", 100, type = "continuous")) +
labs(title = "COVID-19 Deaths")
)Exercises
# Load 9/26 data
report_9_26 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-26-2020.csv")) %>%
rename(Long = "Long_")## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )# Summarize counts for each country
confirmed_report_9_26 <- report_9_26%>%
filter(Lat != "NA", Long != "NA") %>%
group_by(Country_Region) %>%
summarize(Confirmed = sum(Confirmed),
Deaths = sum(Deaths),
Lat = median(Lat),
Long = median(Long))## `summarise()` ungrouping output (override with `.groups` argument)confirmed_report_9_26## # A tibble: 186 x 5
## Country_Region Confirmed Deaths Lat Long
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Afghanistan 39192 1453 33.9 67.7
## 2 Albania 13153 375 41.2 20.2
## 3 Algeria 50914 1711 28.0 1.66
## 4 Andorra 1836 53 42.5 1.52
## 5 Angola 4672 171 -11.2 17.9
## 6 Antigua and Barbuda 98 3 17.1 -61.8
## 7 Argentina 702484 15543 -38.4 -63.6
## 8 Armenia 49072 948 40.1 45.0
## 9 Australia 27040 872 -34.4 146.
## 10 Austria 42214 787 47.5 14.6
## # ... with 176 more rows# map the data
ggplot(data = report_9_26, mapping = aes(x = Long, y = Lat, size = Deaths/1000)) +
borders("world", colour = NA, fill = "grey90") +
theme_bw() +
geom_point(shape = 21, color='red', fill='red', alpha = 0.5) +
labs(title = 'World COVID-19 Deaths',x = '', y = '',
size="Cases (x1000))") +
theme(legend.position = "right") +
coord_fixed(ratio=1.5)## Warning: Removed 81 rows containing missing values (geom_point).
US_cases_9_26 <- report_9_26 %>%
# Filter out non-continental US
filter(Country_Region == "US", !(Province_State %in% c("Alaska","Hawaii", "American Samoa",
"Puerto Rico","Northern Mariana Islands",
"Virgin Islands", "Recovered", "Guam", "Grand Princess",
"District of Columbia", "Diamond Princess")), Lat > 0)mybreaks <- c(10, 100, 1000, 10000, 100000, 100000)
ggplot(US_cases_9_26, aes(x = Long, y = Lat, size = Confirmed/10000)) +
borders("state", colour = "white", fill = "grey90") +
geom_point(aes(x=Long, y=Lat, size=Confirmed, color=Confirmed),stroke=F, alpha=0.7) +
scale_size_continuous(name="Cases", trans="log", range=c(1,5),
breaks=mybreaks, labels = c("10-99",
"100-999", "1,000-9,999", "10,000-99,999", "50,000+", "100,000+")) +
scale_color_viridis_c(option="viridis",name="Cases",
trans="log", breaks=mybreaks, labels = c("10-99",
"100-999", "1,000-9,999", "10,000-99,999", "50,000+", "100,000+")) +
# Cleaning up the graph
theme_void() +
guides( colour = guide_legend()) +
labs(title = "Anisa Dhana's lagout for COVID-19 Confirmed Cases in the US'") +
theme(
legend.position = "bottom",
text = element_text(color = "#22211d"),
plot.background = element_rect(fill = "#ffffff", color = NA),
panel.background = element_rect(fill = "#ffffff", color = NA),
legend.background = element_rect(fill = "#ffffff", color = NA)
) +
coord_fixed(ratio=1.5)## Warning: Transformation introduced infinite values in discrete y-axis
## Warning: Transformation introduced infinite values in discrete y-axis## Warning in sqrt(x): NaNs produced## Warning: Removed 6 rows containing missing values (geom_point).
US_Counties_9_26 <- report_9_26 %>%
unite(Key, Admin2, Province_State, sep = ".") %>%
group_by(Key) %>%
summarize(Confirmed = sum(Confirmed)) %>%
mutate(Key = tolower(Key))## `summarise()` ungrouping output (override with `.groups` argument)# get and format the map data
us <- map_data("state")
counties <- map_data("county") %>%
unite(Key, subregion, region, sep = ".", remove = FALSE)
# Join the 2 tibbles
state_join <- left_join(counties, US_Counties_9_26, by = c("Key"))
ggplot(data = us, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
# Add data layer
borders("state", colour = "black") +
geom_polygon(data = state_join, aes(fill = Confirmed)) +
scale_fill_gradientn(colors = brewer.pal(n = 5, name = "RdBu"),
breaks = c(1, 10, 100, 1000, 10000, 100000),
trans = "log10", na.value = "White") +
ggtitle("Number of Confirmed Cases by US County") +
theme_classic() ## Warning: Transformation introduced infinite values in discrete y-axis
florida_9_26 <- report_9_26 %>%
filter(Province_State == "Florida") %>%
group_by(Admin2) %>%
summarize(Confirmed = sum(Confirmed)) %>%
mutate(Admin2 = tolower(Admin2))## `summarise()` ungrouping output (override with `.groups` argument)us <- map_data("state")
fl_us <- subset(us, region == "florida")
counties <- map_data("county")
fl_county <- subset(counties, region == "florida")
state_join <- left_join(fl_county, florida_9_26, by = c("subregion" = "Admin2")) ggplotly(
ggplot(data = fl_county, mapping = aes(x = long, y = lat, group = group)) +
coord_fixed(1.3) +
geom_polygon(data = state_join, aes(fill = Confirmed), color = "black") +
scale_fill_gradientn(colors = wes_palette("FantasticFox1", 100, type = "continuous")) +
# Cleaning up graph
labs(title = "COVID-19 Cases in FL", x = NULL, y = NULL) +
theme_classic() +
theme(axis.ticks = element_blank())
)- Create a report with static maps and interactive graphs that is meant to be read by others (e.g. your friends and family). Hide warnings, messages and even the code you used so that it is readable. Included references. Link to the Lab 6 report from your Github site. Submit the link to Moodle. Hide warnings, messages and even the code you used so that it is readable. Included references. Link to the Lab 6 report from your Github site. Submit the link to Moodle. I will talk more about the format on Wed.
References
(JHU CSSE 2020)
JHU CSSE. 2020. “CSSEGISandData/Covid-19.” Github Repository. https://github.com/CSSEGISandData/COVID-19.